Method to obtain control data for personized operation of a self-driving motor vehicle

ABSTRACT

Disclosed are methods and a system for customizing operation of a self-driving motor vehicle according to operation behaviors preferred by an individual passenger wherever needed, promising to provide an experience as if the self-driving motor vehicle is driven by the mind of the passenger the first time it operates on a public roadway.

TECHNICAL FIELD

Artificial intelligence; self-driving motor vehicles.

BACKGROUND

Driving automation based on artificial intelligence has evolved now to astage of road tests by self-driving motor vehicle manufacturers. Amongother issues, accidents are occasionally reported calling for moreimprovements. A self-driving motor vehicle could be viewed as if a robotsits on a conventional motor vehicle, though it does not take the shapeof what is commonly presented or perceived comprising a Sensing System,a Control System and an Activation System, while the conventional motorvehicle should be altered significantly for a better integration, asillustrated in FIG. 1. A self-driving motor vehicle drives itself fromone start point to a destination set by a user or a remote controllerthrough a wireless communication system or an electronic media deviceand guided by an automatic navigational system with or without involvinga user in the vehicle. It can carry one or more passengers or nopassengers, for example when it is sent for a passenger. A robot on theself-driving motor vehicle monitors the scenarios in driving includingdetecting roadway conditions and traffic signs and/or signals andvarious other factors that impact its operation, matching againstscenarios modelled in its internal data structures and determines properoperation behaviors according to the traffic rules, just like humandrivers do. However, driving as a human activity has more attributesthan just moving or transportation, comprising safety, comfort,exercise, sport and so on, which vary according to experiences, favors,moral and/or ethics traits of individual drivers or passengers amongother things. In a scenario of an emergency or an accident, differentpassengers or riders tend to have different preferred operationbehaviors by a self-driving motor vehicle, concerning responsibilities,liabilities, and damage controls to different parties involved, andpossible other issues of conflicting interest, which could be verydifficult if ever possible for a self-driving motor vehicle with genericfactory settings to render operation behaviors preferred by eachindividual passenger or rider in such a scenario. From vehicle operationpoint of view, a fundamental difference between a conventional and aself-driving motor vehicle is that the former provides an essentialplatform for a driver to exercise the operation, while the latter triesto provide a ubiquitous platform essentially without involving a driverin its operation. Although there have been vigorous researches onself-driving motor vehicles adapting to a passenger or rider after it ison the road in the state of art technologies, rare work is reported oncustomizing a self-driving motor vehicle in manufacture of aself-driving motor vehicle or before a self-driving motor vehicle ispractically used by a user. There is no country or area in the worldwhere a vehicle licensee has been issued to a self-driving motor vehicletoday.

SUMMARY OF THE INVENTION

A key idea for this invention is to have a self-driving motor vehiclemanufactured and/or trained as if being driven by the mind instead ofhands of each passenger it serves the first day on a journey.

Disclosed is a method of customizing a self-driving motor vehicle bypersonalizing and/or disciplining the self-driving motor vehicle beforethe self-driving motor vehicle is practically used in a service on apublic roadway and applying and/or refining the customizing duringdriving.

Introduced is a customized driving protocol for self-driving motorvehicles comprising:

-   -   obtaining a training collection of data of a plurality of        scenarios and a collection of data of operation behaviors of a        self-driving motor vehicle in each of the scenarios, wherein the        training collection are verified and/or verifiable by simulation        and/or road tests and the operation behaviors of a self-driving        motor vehicle in each of the scenarios are lawful;    -   acquiring a scenario-user-choice pair data set based on the        training collection and acquiring a user profile data set of one        or more users of the self-driving motor vehicle in manufacturing        the self-driving motor vehicle and/or prior to driving the        self-driving motor vehicle on a public roadway in a practical        service;    -   identifying a current rider, or one of current riders of the        self-driving motor vehicle to be the current user, wherein data        have been acquired in the entry of the current user in the        scenario-user-choice pair data set and in the entry of the        current user in the user profile data set of the self-driving        motor vehicle prior to the self-driving motor vehicle being        practically used on a public roadway;    -   driving the self-driving motor vehicle based on the        scenario-user-choice pair data set and/or the user profile data        set, comprising:    -   finding a match between a current scenario and a scenario in a        scenario-user-choice pair        in the entry of the current user in the scenario-user-choice        pair data set;    -   operating the self-driving motor vehicle according to the user        choice in the scenario-user-choice pair if the match is found,        and the current user assumes at least partial responsibilities        for consequences of the operating, or    -   generating operation behaviors of the self-driving motor vehicle        if the match is not found and estimating probability for the        current user to choose each of the operation behaviors        referencing data in the entry of the current user in the user        profile data set and data from statistical analysis of data of        motor vehicle driving records and/or psychological behavior of        drivers, and electing an operation behavior having the largest        probability to operate the self-driving motor vehicle.

Disclosed is also a system for customizing and legalizing a self-drivingmotor vehicle based on the customized driving protocol.

An embodiment of designing a training collection and embodiments ofmatching a scenario are also presented.

BRIEF DISCUSSION OF DRAWINGS

FIG. 1 Illustration of a functional structure of a self-driving motorvehicle.

FIG. 2 Illustration of categorized response time interval to roadway andtraffic events, the shaded area around T1 and T2 indicate it should beconsidered as a zone with a boundary varying from model to model, andfrom time to time.

FIG. 3 Illustration of the procedures to customize a self-driving motorvehicle.

FIG. 4 Table 1, Example impacts on operation of a self-driving motorvehicle by the user data sets.

FIG. 5 Illustration of how to apply user data in self-driving.

FIG. 6 Illustration of the process for legalizing a self-driving motorvehicle.

FIG. 7 Illustration of a comparison of different operation behaviors ofcustomized and non-customized generic self-driving motor vehicles.

FIG. 8 Illustration an expansion of FIG. 7. Module 881 indicating aprobability density distribution of user ethics groups. Module 818indicate the probabilistic relationship between user traits to userpreferred operation behaviors in design of a training collection and inapplying the user profile data assisting vehicle operation.

DETAILED DESCRIPTION OF THE INVENTION

The following descriptions and illustrated example embodiments areintended to explain the invention without limiting the scope of thisinvention.

Scenario hereafter in this disclosure is defined as a data set of asnapshot or a sequence of snapshots of factors in a driving situationthat impact a driving operation of a self-driving motor vehicle,comprising for example: natural environment comprising seasons, weather,and temperature and humidity; social environment comprising socialorder, governing laws including traffic laws and rules, and communityethics in driving; roadway conditions comprising number of lanes, localdriveways or divided multiple lane freeways, ground types, trafficcontrol signals, road signs and obstacles, roadway sharing objectscomprising pedestrians, non-motor vehicles, other motor vehicles,sideway scenes, sideway buildings or other objects and/or views thatcould block the sight of a driver or the Sensing System of aself-driving motor vehicle; traffic conditions comprising the visibilityof conventional driver and of a self-driving motor vehicle, degree ofcongestion, the traffic speed and number of passing motorvehicles/number of lanes in a road section; operation conditions of aself-driving motor vehicle comprising speed, response time to drivingevents, mechanical and electrical performances and number of passengerson board. The data set of a scenario comprises a descriptive data partand optionally an implementation-dependent numeric data part, which aremeasured, categorized, encoded, quantized and structured numeric data ofdata in the descriptive data part.

A user denotes a passenger and/or a rider and/or an owner of aself-driving motor vehicle who is also a passenger or a rider.

A training collection denotes a training collection of data of aplurality of scenarios and a collection of data of operation behaviorsof a self-driving motor vehicle in each of the scenarios, wherein theoperation behaviors comprising one basic vehicle operation or asynchronized sequence of basic vehicle operations comprising speedingup, speeding down, moving forward or backing up, making turns, braking,lights and sound controls, and the data comprising a descriptive datapart and optionally an implementation-dependent numeric data part, whichcomprises measured, categorized, encoded, quantized and structurednumeric data of data in the descriptive data part. Further, as anexample of design, a training collection may comprise a CoordinateMatching Range Vector Set associating with each scenario and each ofoperation behaviors in the scenario for facilitating scenario matchingduring a driving.

A self-driving motor vehicle keeps monitoring scenarios during a drivingincluding roadway traffic and the vehicle conditions by its SensingSystem, and any event prompting for a responding adjustment of itsoperation could be analyzed to fall into one of the three conceptuallycategorized response time intervals, taking into account the distance ofan involved object to and the speed of the vehicle, the time needed forthe robot to run algorithms and Activation System, and for theactivation to take effect, as illustrated in FIG. 2. The parametersseparating the zones are a range of values overlapping between theadjacent zones, which are vehicle model dependent and scenariodependent. The interval between time 0 to T1 is hereby referred to asThe Blinking Zone, wherein the robot can virtually do little or nothingto address an event or avoid an accident except to minimize the damagesand send out alarms if there is an accident. The interval between T1 toT2 is referred to as The Emergency Zone, wherein actions could be takento address an event or avoid an accident or let an accident happen inone way or another that would put different risks of damages to theuser, the vehicle of the user and/or other parties who are involved inthe accident comprising other vehicles or pedestrians who happens toshare the roadway at the time. The interval from T2 beyond is referredto as the Cruise Zone, wherein the roadway and traffic events are easilymanageable, and chance of an accident is very small. Corresponding toeach interval, there are sets of data acquired reflecting attributes ofa user comprising preferred behaviors in various scenarios, preferreddriving styles, and/or moral or ethics traits, which will be used by therobot in control of the vehicle operations.

From customizing vehicle operation point of view, scenarios could bealternatively categorized into a first set named hereby as the Class IScenarios, wherein driving could be managed by a Control Systemessentially based on a generic factory design, and a second set namedhereby as the Class II Scenarios wherein a self-driving motor vehicleoperates driving comprising requiring according to a customized drivingprotocol. The scenarios in the second set comprise for example presenceof a conflict of interests involving attributes of a user comprisingmoral and/or ethics traits, and/or traffic rules and laws, and/or risksto safety of a user and/or a self-driving motor vehicle and/or otherparties sharing roadways, and/or presence of an uncertainty of operatinga self-driving motor vehicle to match the intention of a user inresponse to abrupt events during a driving. To optimize driving bycustomizing the operation behavior of a self-driving motor vehicle, theself-driving motor vehicle is trained prior to its being granted alicense to carry a passenger in a practical service on a public roadwayby two distinctive yet correlated user data sets comprising a userprofile data set and a scenario-user-choice pair data set. The term ofscenario-user-choice pair is an abbreviation for “scenario and userchoice pair” indicating a paired combination between a scenario and apreferred user choice of an operation behavior of a self-driving motorvehicle in the scenario, as illustrated in FIG. 7. Ascenario-user-choice pair data set comprise entries of all users,wherein each entry matches an individual user comprising all thescenario-user-choice pairs of the user in a form of a data structure.Subsequently, each scenario-user-choice pair comprises a descriptivedata part of a scenario and a user choice of an operation behavior inthe scenario and optionally an implementation-dependent numeric datapart comprising measured, categorized, encoded, quantized and structurednumeric data of data in the descriptive data part. A user profile dataset comprises entries of all users, wherein each entry matches anindividual user comprising a user background section and a user traitssection, and each section is comprised a descriptive data part andoptionally an implementation-dependent numeric data part comprisingmeasured, categorized, encoded, quantized and structured numeric data ofdata in the descriptive data part in both sections. The organization ofthe data structures in the data sets is intended to make data in thedescriptive data part accessible by any implementers, and as aninterface to users during training self-driving motor vehicles, whiledata in the numeric data part is for facilitating real-time processingby a Control System, and therefore is implementation-dependent. Theconcept behind a scenario-user-choice pair comes from the observationthat there could be multiple options to operate the vehicle in ascenario, and a Control System might have difficulty to figure out anoptimal solution matching the intention of a current user, without priorknowledge of the attributes and/or preferences of the current user. Thebackground section of a user profile data comprises data of a userincluding age, gender, body height, body weight, profession, marriagestatus, living area, education level, searchable public recordscomprising of driving, medical, disability, insurance, credit, andcrimes; while the traits section comprises data of a user of drivingand/or riding styles, and/or the moral and/or ethics traits of a user.

To acquire a scenario-user-choice pair data set, a training collectionis obtained by designing a training collection and/or receiving adesigned training collection or a combination of designing andreceiving. A training collection are verified and/or verifiable bysimulation and/or road tests and each of the collection of operationbehaviors of a self-driving motor vehicle in each of the scenarios in atraining collection are lawful. Design of a training collection andverification of a training collection by simulation and/or analysis ofthe statistical driving data is feasible within the state of the art,while verification by road tests is also feasible in at least part ofthe scenarios in a training collection, though less efficient. Atraining collection could be designed by a manufacturer of self-drivingmotor vehicles and/or an institution other than a manufacturer ofself-driving motor vehicles and/or an individual designer for a specificvehicle model or as a design for a range of vehicle models, inaccordance with the laws and rules of areas or countries wherever theself-driving motor vehicles are to be used by applying sophisticatedalgorithms of artificial intelligence or by trained professionalsoperating some relatively simple statistical tools processing data fromconventional vehicle driving records, simulation and/or road tests ofself-driving motor vehicles, community driving behaviors and/or moraland/or ethics traits of the areas and/or the countries.

Below is a description of an embodiment of such a design, which servesto illustrate the invention without limiting the scope of the invention.

C1. picking up scenarios into a training collection:

-   -   collecting data of a collection of scenarios into a candidate        set of a training collection based on a statistical analysis of        data of motor vehicle driving records, and/or data from        simulation of motor vehicle driving;    -   assigning a first weighting factor Wc11 to each of the scenarios        in the set, wherein the value of Wc11 comprising proportional to        appearance probability of the scenario based on a statistical        analysis of the data of the scenarios in the candidate set;    -   assigning a second weighting factor Wc12 to each of the        scenarios, wherein the value of Wc12 comprising proportional to        level of risk of safety of and/or damage to properties of        parties involved in the scenario in relation to the intention of        different drivers operating a motor vehicle in the scenario        based on a statistical analysis of the data of the scenarios in        the candidate set;    -   assigning a third weighting factor Wc13 to a scenario, wherein        the value of Wc13 comprising proportional to level of uncertain        of operating a motor vehicle in relation to the intention of        different drivers in response to abrupt driving events in the        scenario based on a statistical analysis of the data of the        scenarios in the candidate set;    -   finding a combined weight Wc10 comprising by a weighting average        of the three weighting factors;    -   sorting the candidate set in a descending order of the combined        weight Wc10;        selecting data of scenarios in the candidate set into the        training collection referencing in a top-down order to the        combined weight Wc10, until the combined weight Wc10 being        smaller than a first adjustable threshold Wc1t;

C2. finding a candidate data set of a collection of lawful operationbehaviors in each scenario in the training collection:

-   -   finding a candidate data set of operation behaviors for each        scenario in the training collection from a collection of data of        operation behaviors in the scenario based on a statistical        analysis of data of motor vehicle driving records, and/or data        from simulation of motor vehicle driving;    -   finding the probability of appearance of each of the operation        behaviors in the candidate set by a statistical analysis;    -   removing from the candidate set of data of operation behaviors        unlawful operation behaviors;    -   removing from the candidate set of data of operation behaviors        with probability of appearance smaller than a second adjustable        threshold Pc2;

C3. establishing user groups with similar psychological behavior patternof moral and/or ethics traits:

-   -   establishing a psychological behavior probability density        distribution based on a statistical analysis of data of motor        vehicle driving records, and/or data from simulation of motor        vehicle driving relating the moral and/or ethics traits of a        congregation of drivers and/or passengers, comprising:    -   forming a one-dimensional probability density distribution        between extreme selfish at one side and altruism at the other        side, or a multiple dimensional user psychological behavior        probability density distribution, and    -   dividing by an adjustable segment probability value the entire        distribution domain into a plurality of segments wherein each        segment corresponding to a congregation of a group of users with        similar psychological behavior pattern of moral and/or ethics        traits;    -   removing user groups with a segment probability smaller than a        third adjustable threshold Pc3;

C4. electing an operation behavior from the candidate set of operationbehaviors associating with data of a scenario in a training collection:

-   -   determining based on a statistical analysis of data of motor        vehicle driving records, and/or data from simulation of motor        vehicle driving and data of driving style and/or moral and/or        ethics traits of drivers and/or passengers a probability        P32[i][j] for a resulting user group i in step C3 to select an        operation behavior j in the resulting candidate set of data of        operation behaviors in step C2,    -   wherein i=(1, 2 . . . , L), j=(1, 2 . . . , M); L denotes the        number of the total resulting user groups in step of C3 and M        denotes the number of total operation behaviors in a resulting        candidate set in step of C2;    -   electing an operation behavior from the resulting candidate set        in step of C2 into the training collection as an operation        behavior for a user to choose to form a scenario-user-choice        pair if the probability P32[i][j] being larger than an        adjustable threshold P32t, or a user group dependent adjustable        threshold P32t[i];    -   removing from the resulting candidate set the operation behavior        elected above to avoid duplicating;

C5. optimizing the design for achieving a balancing between coverage ofthe scopes of the data and efficiency in actual usage of a trainingcollection by adjusting the involved thresholds and factors;

C6. calculating a Coordinate Matching Range Vector Set associating withdata of each scenario and data of each operation behavior in thescenario in the training collection for scenario matching in a driving,wherein:

an algorithm for scenario matching comprising:

-   -   representing by a vector Cij in real space the numeric data part        of a scenario in a training collection measured, categorized,        encoded, quantized and structured using a protocol of data in        the descriptive data part of the scenario,        Cij=[1], . . . Cij[n]),  [1]    -   wherein Cij [k] is the coordinate of the K^(th) component of        vector Cij and        (k=1,2 . . . n),0<Cij[k]≤1;    -   representing by a vector of n dimension Ci in real space the        numeric data part of a current scenario measured, categorized,        encoded, quantized and structured using the same protocol of        data in the descriptive data part of the scenario,        Ci=(Ci[1],Ci[n]),  [2]    -   wherein Ci[k] is coordinate of the K^(th) component of vector Ci        and (k=1, 2 . . . n), 0<Ci[k]≤1; and hereafter in this example        implementation, any coordinate segment between two coordinate        boundary values of a component of a vector form a coordinate        range of the component of the vector, wherein each coordinate        represent a corresponding monotonic measurement of a factor of        the data set of a scenario, such that if any two boundary values        of a coordinate segment satisfy a matching condition, all the        coordinates within the segment satisfy the matching condition;    -   letting Sij representing a similarity measurement between the        two vectors Ci and Cij, and        Sij=(Σ(Ci[k]−Cij[k]){circumflex over ( )}2×α_(k))){circumflex        over ( )}0.5, (k=1,2, . . . ,n),   [3]    -   wherein α_(k) is a weighting factor for coordinate of the K^(th)        component,        0<Cij[k]≤1; 0<Ci[k]≤1; 0<α_(k)≤1;    -   if Sij is smaller than an adjustable threshold Tij:    -   determining an adjustable Matching Boundary Vector Cijb around        Cij based on a statistical analysis of data of motor vehicle        records and/or data from simulations and road tests of motor        vehicles, and        Cijb=[(Cijbmin[1],Cijbmax[1]), . . . (Cijbmin[n],Cijbmax[n])],          [4]    -   wherein 0<Cijbmin[k]≤1, 0<Cijbmax[k]≤1, and

Cijbmin[k] is the minimum coordinate of the k^(th) component of Cijb,and Cijbmax[k] is the maximum coordinate of the k component of Cijb, (k1, 2, . . . , n);Cijbmin[k]≤Cij[k]≤Cijbmax[k], (k=2, . . . ,n);and Ci[k] is also within a range of a Matching Boundary Vector Cijb,Cijb min[k]≤Ci[k]≤Cijbmax[k], (k=1,2, . . . ,n);

-   -   letting Pij representing an operation behavior in scenario Cij,        if Pij is as effective in scenario Ci or the probability for Pij        to be as effective in scenario Ci is larger than an adjustable        threshold Wij, asserting scenario Ci to be a match for scenario        Cij under the condition of Pij;    -   narrowing down and/or refining the search range comprising:    -   using a range granularity adjusting factor m equally dividing        the range of each coordinate of each component of Coordinates        Matching Range Vector Cijb into m congruent segments with a        range threshold Tijbs[k] calculated by:        Tijbs[k]=(Cijbmax[k]−Cijbmin[k])/m; (k=1,2, . . . n);   [5]        expanding the Coordinates Matching Range Vector Cijb into an        array Cijba comprising coordinate segments with smaller range        values in each component, and Cijba=

$\begin{matrix}{{Cijba} = \begin{matrix}{\left( {{{{{Cijb}\min}\lbrack 1\rbrack}\lbrack 1\rbrack},{{{{Cijb}\max}\lbrack 1\rbrack}\lbrack 1\rbrack}} \right),} & {\left( {{{{{Cijb}\min}\lbrack 1\rbrack}\lbrack 2\rbrack},{{{{Cijb}\max}\lbrack 1\rbrack}\lbrack 2\rbrack}} \right),} & {\ldots\mspace{14mu},} & \left( {{{{{Cijb}\min}\lbrack 1\rbrack}\lbrack m\rbrack},{{{{Cijb}\max}\lbrack 1\rbrack}\lbrack m\rbrack}} \right) \\\vdots & \vdots & {\ldots\mspace{20mu},} & \vdots \\\left( {{{{{Cijb}\min}\lbrack n\rbrack}\lbrack 1\rbrack},{{{{Cijb}\max}\lbrack n\rbrack}\lbrack 1\rbrack}} \right) & \left( {{{{{Cijb}\min}\lbrack n\rbrack}\lbrack 2\rbrack},{{{{Cijb}\max}\lbrack n\rbrack}\lbrack n\rbrack}} \right) & {\ldots\mspace{14mu},} & \left( {{{{{Cijb}\min}\lbrack n\rbrack}\lbrack m\rbrack},{{{{Cijb}\max}\lbrack n\rbrack}\lbrack m\rbrack}} \right)\end{matrix}} & \lbrack 6\rbrack\end{matrix}$wherein,

-   -   Cijbmin[k][1] is the minimum value of the L^(t) segment of the        K^(th) component of the Coordinates Matching Range Vector Cijb,        and    -   Cijbmax[k][1] is the maximum value of the L^(th) segment of the        K^(th) component of the Coordinates Matching Range Vector Cijb,        and        Cijbmax[k][1]=Cijbmin[k][l+1], (k=1,2, . . . n); (l=1,2, . . .        ,m−1),    -   Cijbmax [k] [1] is equal to Cijbmin[k]; and Cijbmax [k] [m] is        equal to Cijbmax[k], respectively;    -   constructing a candidate matching vector of a scenario C_(ji)        comprised of n coordinates with each of the coordinates from a        coordinate of a boundary point in a row of Cijba;    -   finding all the vector C_(ji) matching Cij based on the        algorithm for scenario matching through a maximum of        n{circumflex over ( )}m tests and putting the matching vectors        into a candidate set;    -   constructing a Coordinate Matching Range Vector Cijr, wherein        each coordinate is composed of a pair of coordinates comprising        a first coordinate from a coordinate of a component of a vector        in the candidate set and a second coordinate from a coordinate        of the same component of vector Cij, wherein the range of the        pair of coordinates comprising a matching range of the        coordinate values of the component;    -   fine-tuning the range granularity adjusting factor m to achieve        a compromise between the granularity of search range, the        computation complexity online and offline, and the data size of        Coordinate Matching Range Vectors;

C7. organizing all Coordinate Matching Range Vectors into a structuredCoordinate Matching Range Vector Set Cijra associating with data of eachof the scenarios and data of each of the operation behaviors in the eachof the scenarios in the training collection.

For a scenario-user-choice pair in a scenario-user-choice pair data set,the Coordinate Matching Range Vector Set that comes along with ascenario-user-choice pair demands a considerable real-time storagespace. However, generating the Coordinate Matching Range Vector Setcould be conducted off the line and/or during design of a trainingcollection, which does not consume real-time computing resources. Thesedata sets could be further compressed and structured prior to and/orafter being acquired in a scenario-user-choice pair data set and storedin a real-time storage media. A combination of a real-time matchingalgorithm with a searching-based matching could provide a balancedspace-time trade-off.

It should be noted that the above embodiment of designing a trainingcollection is only an illustrative implementation, and different methodscomprising multilayered relational data base could be deployed to getscenarios measured, categorized, encoded, quantized and structured, andscenarios matching could find various implementations in state of artself-driving motor vehicles designs by techniques including machinelearning and artificial intelligence.

Customizing a self-driving motor vehicle starts by acquiring ascenario-user-choice pair data set based on a training collection in aninitialization process, which could take place in manufacture of aself-driving motor vehicle and/or before the vehicle is practically usedon a public roadway. The initialization process is carried out by atesting apparatus comprising a stand-alone testing apparatus and/or ahuman tester operating a testing apparatus and/or a robot of aself-driving motor vehicle, using a multi-media human-machine interfaceconducting the steps of:

-   -   identifying a user;    -   informing the user of a user choice of an operation behavior in        a scenario comprising a binding commitment between a        self-driving motor vehicle and the user, wherein the        self-driving motor vehicle operates according to the operation        behavior of the user choice in a matched scenario, the user        assumes at least partial responsibility for the consequence of        the operation behavior;    -   obtaining a consent from the user to the binding commitment;    -   presenting to the user data of one scenario at a time of the        scenarios in a training collection and data of each of operation        behaviors of a self-driving vehicle in the scenario in the        training collection;    -   informing the user of the at least partial responsibility for        the consequence of the each of the operation behaviors in the        scenario;    -   obtaining a choice from the user of an operation behavior in the        scenario to form a scenario-user-choice pair of the scenario and        the operation behavior;    -   storing data of the scenario-user-choice pair in an entry of the        user in a scenario-user-choice pair data set;    -   repeating the steps from presenting to storing for every        scenario in the training collection; and/or        receiving obtained data of scenario-user-choice pairs into an        entry of the user in a scenario-user-choice pair data set of a        self-driving motor vehicle and confirming and/or updating the        scenario-user-choice pair data set with the user prior to the        self-driving motor vehicle being practically used by the user in        a service on a public roadway.

A description of the partial responsibility and/or consequence of theoperating for a scenario-user-choice pair is illustrated in The Example1 below.

The interactive interface between a testing apparatus and the user couldbe of a visual media comprising a touch screen panel for display andinput, or an audio media comprising a speaker announcement combined witha microphone and a speech recognition module to take the inputs, or acombination thereof, for users without vision or hearing disabilities.For user with disabilities, however, an assistant to the user could helpwith the initialization to use the above interactive interfaces, or anadaptive device could be designed and installed.

In addition to a scenario-user-choice pair data set, a user profile dataset is also acquired in an initialization process. Data in thebackground section of a user profile data set are acquired before a userpurchases or is granted a permit to use a service of a self-drivingmotor vehicle and/or before a user driving a self-driving motor vehicleson a public roadway in a practical service, by a testing apparatusthrough a human-machine interactive interface to obtain informationprovided by a user and/or research by a testing apparatus through awireless communication system or an electronic media device whereinrelevant user data are stored. The obtained background data of the userare stored in the background section of the entry of the user in a userprofile data set.

A testing apparatus extracts trait of the user by analyzing the acquireddata of the user in the scenario-user-choice pair data set andbackground data in the background section of the user profile data setbased on behavior modeling, factory tests and statistical drivingrecords and get the extracted user traits data stored in the traitssection of the entry of the user in a user profile data set. Thescenario-user-choice pair data set and/or the user profile data set datasets could be partially or fully acquired in manufacture of aself-driving motor vehicle and/or prior to a user purchasing aself-driving motor vehicle and delivered to the robot of a self-drivingmotor vehicle and be confirmed and updated if necessary by a testingapparatus and a current user before a self-driving motor vehicle ispractically used on a public roadway.

An example of impact on operation by scenario-user-choice pair data isillustrated in FIG. 4 Table 1. Since how to handle events in theEmergency Zone between T1 and T2 is most critical and controversial tothe safety behavior of a self-driving motor vehicle, some examples aredesigned and given below as an illustration.

Example 1: A self-driving motor vehicle is driving on a roadway at anormal speed approaching an intersection with a green light, a bicyclesuddenly runs red light from one side of the roadway appearing in frontof the self-driving motor vehicle. The robot finds braking the vehicleis too late to avoid the accident, but swing the vehicle to the left orright might have a chance, which would violate the traffic rules byrunning into a wrong lane and have a chance to damage the self-drivingmotor vehicle, which would be your choice:

-   A. Brake the vehicle-   B. Swing the vehicle.

Example 2: At what risk degree between 0 and 1 would you take to damageyour vehicle or harm yourself to avoid running over pedestrians (0indicates none, 1 indicates full)?

-   A. 0-   B. 1-   C. 0.5-   D. Undecided.

Example 3: When a collision between the self-driving motor vehicle andanother vehicle is unavoidable, which of the following would you choose?

-   A. Minimize the damage to yourself no matter what happens to the    other party-   B. Minimize the damage to yourself no matter what happens to the    other party if the other party has the liability-   C. Take some risk of damaging yourself depending the circumstances    to reduce the damage to the other party.

Example 4: When an accident is unavoidable, which of the following wouldyou choose?

-   A. Minimize the damage to the passenger sitting on the front-left    seat B. Minimize the damage to the passenger sitting on the    back-right seat-   C. Minimize the damage to myself no matter where I am sitting.

Example 5: Your preferred driving style in highway is:

-   A. Smooth and steady-   B. Swift and jerky-   C. Aggressive with sport flavor.

An illustration to how to apply the two sets of user data in a real-timeoperation is given in FIG. 5. A robot or a service provider or a user ofa self-driving motor vehicle identifies a current user to be the currentuser, by identifying a current rider or a passenger, or one of currentriders or passengers of a self-driving motor vehicle being a user havingacquired data in the entry of the user in a scenario-user-choice pairdata set and/or having acquired data in the entry of the user in ascenario-user-choice pair data set and the entry of the user in a userprofile data set prior to a self-driving motor vehicle being practicallyused on a public roadway. When multiple users are riding a self-drivingmotor vehicle, it is mandatory to select one of the riders as thecurrent user and apply scenario-user-choice pair data of the currentuser in the scenario-user-choice pair data set or refer to user profiledata of the current user in the user profile data set in assisting theoperation of the vehicle. In case there is no passenger riding thevehicle, a default or selective set of factory settings, orscenario-user-choice pair data and/or user profile data of a designateduser could be elected in assisting the vehicle operation.

A self-driving motor vehicle should follow the rules and laws regardingvehicle operations in the first place. In response to a scenario duringa driving, the robot tries firstly to find a match between a currentscenario and a scenario in a scenario-user-choice pair in the entry ofthe current user in the scenario-user-choice pair data set, and operatesthe self-driving motor vehicle according to the operation behavior ofuser choice in the scenario-user-choice pair if the match is found,wherein if the current user is a current rider, the current user assumesat least partial responsibilities for consequences of the operating. Therobot proceeds to generate one or more optional operation behaviors ofthe self-driving motor vehicle if the match is not found, and in case ofmore than one optional operation behaviors being generated, estimateprobability for the current user to choose each of the operationbehaviors referencing data in the entry of the current user in the userprofile data set and data from statistical analysis of data of motorvehicle driving records and/or psychological behavior of drivers, andelecting an operation behavior having the largest probability to operatethe self-driving motor vehicle.

Sensing and matching a current scenario with an internal scenariorepresentation in a self-driving motor vehicle are both rudimentary andessential for operating a self-driving motor vehicle. Given that manytesting self-driving motor vehicles have been on the road for a fewyears, it is safe and sound to assume scenario matching has been anenabling procedure in the prior art of a self-driving motor vehicledesign, though room for improvements exists. Since the intention of thisinvention is to apply scenario matching in customizing a self-drivingmotor vehicle, this disclosure does not intend to elaborate on itsimplementations, though some illustrative examples are introduced belowbased on the illustrated design of a training collection:

Example I

-   -   Generate a vector Ci of a current scenario using the same        protocol to a scenario in the training collection design        embodiment previously presented in this specification,    -   Ci=(Ci[1], . . . Ci[n]) wherein    -   Ci[k] is coordinate of the K^(th) component of Ci, (k=1, 2, . .        . , n), and 0<Ci[k]≤1;    -   Let Cj be a vector of n dimension representing a scenario in the        scenario-user-choice pair data set,    -   Cj=(Cj[1], . . . Cj [n], wherein    -   Cj[k] is coordinate of the K^(th) component Cj, (k=1, 2, . . . ,        n), and 0<Cj [k]≤1.    -   Calculate similarity Sij;    -   Sij=(Σ(Ci[k]−Cj[k]){circumflex over ( )}2×α_(k)/(n×Σα_(k))) 0.5,        (k=1, 2, . . . , n), wherein α_(k) is a weighting factor for the        k^(th) coordinate of Cj, 0<Cj[k]≤1; 0<α_(k)≤1; Ci[k] and Cj[k]        are matching coordinate components of Ci and Cj.    -   if Sij is smaller than an adjustable threshold Tj, and Ci[k] is        within the coordinate range of a Matching Boundary Vector Cjb        which is expressed as,        Cjb=[(Cjmin[1],Cjmax[1]), (Cjmin[n],Cjmax[n])], (k=1, . . . ,n)        wherein 0<Cjmin[k]≤1; 0<Cjmax[k]≤1; Cjmin[k] is the minimum        coordinate value of the k^(th) component of Cjb, Cjmax[k] is the        maximum coordinate value of the k^(th) component of Cjb;    -   Cjmin[k]≤Cj[k]≤Cjmax[k], and if an operation behavior Pj in        scenario Cj is as effective in scenario Ci or the probability        for Pj to be as effective in scenario Ci is larger than a        threshold Wj, scenario Ci is recognized to be a matching        scenario for scenario Cj.

Example II

An alternative example embodiment to find a match of a current scenarioto a scenario in the scenario-user-choice pair data set is by searchingthrough Coordinate Matching Range Vector Set of all the scenarios in thescenario-user-choice pair data set,

-   -   Generate a vector Ci of a current scenario using the same        protocol to a scenario in the training collection design        embodiment previously presented in this specification,        Ci=(Ci[l], . . . Ci[n]), wherein    -   Ci[k] is coordinate of K^(th) component of Ci, (k=1, 2 . . . . ,        n), and 0<Ci [k]≤1;    -   Search through all Coordinate Matching Range Vector Sets of all        the scenarios in the scenario-user-choice pair data set, if        coordinate of each component of vector Ci is found in a        coordinate range of the corresponding component of a Coordinate        Matching Range Vector of scenario Cj, scenario Cj is recognized        to be a matching scenario for scenario Ci.

A simple example of applying scenario matching in a driving is asfollows:

-   -   for a scenario Ci:    -   a self-driving motor vehicle is driving in a local roadway at a        speed of 25 m/h; a pedestrian is crossing a local roadway 10        meter ahead;    -   operation behavior of user choice Pi in the scenario Ci is:    -   break to stop to avoid an accident;    -   assuming other factors are the same, a matching range of Ci for        driving speed is 28-33 m/hour;    -   if in a current scenario Cj, driving speed is detected to be 32        m/hour, the current scenario Cj is therefore determined to match        scenario Ci and operation behavior of user choice brake to stop        to avoid an accident Pi is executed.

One example of categorizing the traits of a user into one of thefollowing groups in response to events in the Emergency Zone isillustrated in Example 4 as follows:

-   A. Habitual traffic violation offenders-   B. Strict traffic rule followers-   C. Smart and flexible drivers-   D. Altruism volunteer heroes

It should be noted generating based on each category of user traits apreferred operation behavior of the vehicle is only of a probabilitynature. For example, in the Example 1 of the previously listed fiveexample scenarios, although there is a chance with a large probabilityfor traits B group users to select the answer A “brake”, while traits Cgroup users might select the answer B “swing”, it should not be assumedto be an affirmative action, of which the current user is not committedto take any responsibility for the consequence of the operation. Forevents in the Cruise Zone, a categorization based on user driving stylesin Example 5 is as follows:

-   A. Smooth and steady-   B. Swift and jerky-   C. Aggressive with sport flavor,    which could be rendered in the vehicle operations in favor of a user    choice or style when it is safe and lawful.

Certain restrictions are applied as a default setting for self-drivingmotor vehicles in general. For example, since this disclosure is notconcerned about the application to use the driver-less technology for abattle vehicle in a war or for a vehicle for law enforcement, theself-driving motor vehicle is to be inhibited to be engaged in anyoffensive action against any third parties, including pedestrians, othervehicles etc. It should also be barred from any self-destructionbehavior comprising running out of a cliff or against a road barrier orwalls of a building, unless the Control System of the robot determinessuch a move is necessary for reducing the seriousness of an otherwiseunavoidable accident and the current user has optioned a choice of suchan operation behavior in the scenario-user-choice pair data set.Although in general, applying user data in a scenario-user-choice pairdata set or a user profile data set is in operating a self-driving motorvehicle is intended to satisfy experience and expectation of a user,there are exceptions on the contrary, for example, if a user riding aself-driving motor vehicle is found to be drunk by an alcoholic sensor,or to be a habitual reckless driving offender, certain functionsincluding user overriding the robot for manually operating the vehicleshould be restricted.

A continuing user customization by user adaptation and learning duringdriving is illustrated in FIG. 3 by module 380, particularly if thecurrent user is a recurrent passenger or an owner of the vehicle. In afirst example embodiment, a robot notifies the current user by promptingmessages and/or making announcements, through visual, sound or othertypes of media about an unfamiliar and/or untrained and/or hazardousroadway and traffic condition and asks for a guidance or command fromthe current user, executing the guidance or command in operation uponreceiving the guidance or command. Then the robot conducts evaluation ofthe effect or performance of the operation, and if the performance issatisfactory without an accident, generates data in the descriptive datapart of the scenario from the driving records, and takes the guidance orcommand as a user choice of operation behavior to form ascenario-user-choice pair, and with an explicit consent from the user,inserts the descriptive data into the entry of the user in thescenario-user-choice pair data set. An explicit consent from the user isrequired to make sure that the user knows a new data item of ascenario-user-choice pair being added and commits to assume at leastpartial responsibility for the consequence for the operation behavior ina matched scenario as a result of inserting the new data item. Theupdated scenario-user-choice pair data set is checked and used toextract and update data in the traits section of the user profile data,and the updated data sets will be applied in the driving afterwards.

In another example embodiment, the current user could take over thedriving physically when necessary if it is feasible in the design, and asimilar process to that used in the first example embodiment could beused based on a recorded scenario and driving behavior by the currentuser, to update the two user data sets by the same procedures as orsimilar procedures to procedures in the first example, comprising thesteps of:

recording and analyzing data of scenarios and driving behaviors of thecurrent user physically taking over operation of a self-driving motorvehicle; conducting an evaluation of performance of the user operationand generating data of scenario-user-choice pairs based on data of thescenarios and data of the driving behaviors of the current user;inserting data of the scenario-user-choice pairs into the entry of thecurrent user in the scenario-user-choice pair data set with explicitconsent from the current user if the performances being satisfactoryresulting in no accidents.

In another example embodiment, a robot could chat with a user through ahuman-machine interface and/or monitor a gaze, and/or a gesture of thecurrent user, and/or use other procedures to detect and analyze theuser's verbal, and/or tactile, and/or body languages reflecting his orher experiences and/or sentiments during the driving, and tune itsoperation accordingly, and the process could be used to expand and/orupdate the user profile data sets whenever applicable. Thereby, thecustomized operation of a self-driving motor could be incrementallyrefined.

The methods disclosed hereby could help resolve some of thecontroversial legal and/or moral issues including but not limited tothose illustrated in the examples above. For instance, the manufacturerand/or the service provider and/or the insurance provider of aself-driving motor vehicle operating on default factory settings isusually supposed to assume all liabilities for the vehicle being used.However, acquiring and applying scenario-user-choice pair data in ascenario-user-choice pair data set establishes a commitment between thevehicle and a user, wherein if the vehicle faithfully executes a userchoice of an operation behavior in a scenario matching a scenario in ascenario-user-choice pair in the entry of the current user in ascenario-user-choice pair data set, the current user assumes at leastpartial responsibilities for the consequences of the operation, whichcould resolve some of the controversial legal issues in addition tohaving the benefits of reducing areas of uncertainties and complexitiesin a Control System design.

One major hurdle in legalization of self-driving motor vehicles has beenthe concern over their impersonal and/or unpredictable behaviors inhandling abrupt, and/or unpredicted and/or conflicting-interest events.In that regard, an industry standard of a customized driving protocolbased on the methods for customizing driving of self-driving motorvehicles described above could serve as a means of legalizingself-driving motor vehicles, if accepted by the industry and the publicas well after road tests and other necessary evaluation/verificationprocesses. As an evidence of its efficacy of a utility, a trainingcollection used for acquiring a scenario-user-choice pair data set couldserve as a first module and performance criterion for such a protocol,since it reassures and manifests a well-defined verifiable and/orverified lawful operation behaviors accommodating any users, and aninstance of a user customization by acquiring a scenario-user-choicedata set based on a training collection as detailed above will result innothing less than predictable lawful vehicle operations, as if aconventional motor vehicle is driven by a human driver with at least abetter-than-average driving record. A comparison of operation behaviorsbetween a customized and non-customized generic self-driving motorvehicle as illustrated in FIG. 7 serves as a support to the idea,wherein Module 711 denotes a range of uncertain and/or unpredictableoperation behaviors without user customization, within the restraint oftraffic rules and laws, while module 712 comprising 711, denoting arange of operation behaviors with user customization incorporating userattributes, within the restraint of broader laws comprising trafficrules and laws.

Based on core processes of the methods of customized driving describedabove, a customized driving protocol for a self-driving motor vehicle ispresented as follows:

-   -   obtaining a training collection of data of a plurality of        scenarios and a collection of data of operation behaviors of a        self-driving motor vehicle in each of the scenarios, wherein the        training collection are verified and/or verifiable by simulation        and/or road tests and the operation behaviors of a self-driving        motor vehicle in each of the scenarios are lawful;    -   acquiring a scenario-user-choice pair data set based on the        training collection and acquiring a user profile data set of one        or more users of the self-driving motor vehicle in manufacturing        the self-driving motor vehicle and/or prior to driving the        self-driving motor vehicle on a public roadway in a practical        service;    -   identifying a current rider, or one of current riders of the        self-driving motor vehicle to be the current user, wherein data        have been acquired in the entry of the current user in the        scenario-user-choice pair data set and in the entry of the        current user in the user profile data set of the self-driving        motor vehicle prior to the self-driving motor vehicle being        practically used on a public roadway;    -   driving the self-driving motor vehicle based on data in the        scenario-user-choice pair data set and/or the user profile data        set, comprising:    -   finding a match between a current scenario and a scenario in a        scenario-user-choice pair        in the entry of the current user in the scenario-user-choice        pair data set;    -   operating the self-driving motor vehicle according to the user        choice in the scenario-user-choice pair if the match is found,        and the current user assumes at least partial responsibilities        for consequences of the operating, or    -   generating operation behaviors of the self-driving motor vehicle        if the match is not found and estimating probability for the        current user to choose each of the operation behaviors        referencing data in the entry of the current user in the user        profile data set and data from statistical analysis of data of        motor vehicle driving records and/or psychological behavior of        drivers, and electing an operation behavior having the largest        probability to operate the self-driving motor vehicle.

A self-driving motor vehicle operating driving according to thecustomized driving protocol should be eligible to apply for a license orpermit to carry a passenger in a practical service, and a license orpermit could be issued to the self-driving motor vehicle if otherfunctionalities including electromechanical features and performance ofthe self-driving motor vehicle driving in scenarios other than in thetraining collection are also qualified.

Two procedures, namely Procedure I and Procedure II are introducedwherein a manufacturer of self-driving motor vehicles or an authorizedor designated organization and/or individual evaluates if a self-drivingmotor vehicle could meet a first and/or a second performance criterionfor operating according to the customized driving protocol and issuesperformance certificate to the self-driving motor vehicle meeting thefirst and/or the second performance criterion for applying for a licenseor permit to carry a passenger in a practical service, wherein each ofthe two Procedures comprises two similar tests and Procedure Icomprising:

-   -   a first test examining the training collection for acquiring the        scenario-user-choice pair data set adopted by a self-driving        motor vehicle, checking the scope of coverage of the plurality        of scenarios and the scope of coverage of the collection of        operation behaviors in each of the scenarios, wherein the        self-driving motor vehicle passes the test if the scope of        coverage of the plurality of scenarios meets a first performance        benchmark and the scope of the collection of operation behaviors        meets a second performance benchmark, wherein the first        performance benchmark comprises:    -   the plurality of scenarios in the training collection comprise a        sub-set of scenarios in a reference training collection        comprising all available Class II scenarios at the time of the        test and the ratio of the number of the scenarios in the        training collection over the number of the scenarios in the        reference training collection is bigger than a first threshold        Pt1, wherein the reference training collection could be obtained        in a process such as in the example design of a training        collection through the steps of C1-C7.    -   wherein the second performance benchmark comprises:    -   the collection of operation behaviors in a scenario comprise a        sub-set of operation behaviors in the scenario of the reference        training collection and an average value or a weighted average        value respecting all the scenarios in the training collection of        a ratio of the number of operation behaviors in a scenario in        the training collection over the number of the operation        behaviors in the scenario in the reference training collection        is bigger than    -   a second threshold Pt2, wherein values of the weighting factors        to a ratio comprising depending upon the appearance probability        and risk degree of the scenario.    -   a second test is conducted to verify the self-driving motor        vehicle operating driving according to the customized driving        protocol, and if the self-driving motor vehicle operates driving        according to the customized driving protocol, it passes the        second test.    -   the self-driving motor vehicle meets the first performance        criterion if the self-driving motor vehicle passes the first        test and the second test.

In Procedure II, however, a third performance criterion is adopted,wherein the first test examines the scenario-user-choice pair data sethaving acquired data in an entry of the current user in place of thetraining collection, while the second test is the same as in ProcedureI. The data of the current user in the user profile data could also bechecked to determine conditions if or how to provide the service to thecurrent user, wherein the first test comprising:

-   -   examining the scenario-user-choice pair data set having acquired        data in an entry of the current user, wherein the self-driving        motor vehicle passes the first test if the scenarios in an entry        of the current user in the scenario-user-choice pair data set        comprise a sub-set of scenarios in a reference training        collection comprising all available Class II scenarios at the        time of the test and the ratio of the number of the scenarios in        the user entry of the scenario-user-choice pair data set over        the number of scenarios in the reference training collection is        bigger than a third threshold Pt3, wherein the reference        training collection could be obtained in a process such as in        the example design of a training collection through the steps of        C1-C7.

The training collection used as a first performance criterion forlegalizing self-driving motor vehicles could be designed by amanufacturer of self-driving motor vehicles, and/or by an institutionand/or an individual other than a manufacturer of self-driving motorvehicles in accordance with the rules and laws of an area, and/or of acity and/or of a state and/or of a country as a standard performancecriterion.

Design of a training collection could be carried out by complicatedalgorithms or just by ordinary skilled professionals with adequateknowledge, experience and training using relatively simple statisticaltools to process the statistical conventional vehicle operation dataand/or data from simulation and/or road tests of self-driving motorvehicles correlating with moral and/or ethics traits of users andtraffic rules and laws. Variations in controls and maneuverability ofself-driving motor vehicles, and different rules and laws in differentareas and/or countries require distinctive designs for a trainingcollection as a first performance criterion for legalizing self-drivingmotor vehicles. However, the introduced performance criteria as part ofthe methods of customization of self-driving motor vehicles forlegalizing self-driving motor vehicles is effective in increasingreliability and transparency in vehicle operation behaviors and reducingthe worries and panics from the legislators and the public over theirperformance in high risk, uncertain and conflicting-interest scenarios.

Customizing a self-driving motor vehicle in manufacturing comprises anefficient design of a Control System, being capable of fast accessingand processing the data in a scenario-user-choice pair data set, and/ora user profile data set; running fast scenario matching; efficientlyoperating according to a user choice in a matched scenario and/orrunning probabilistic analysis of data in a user profile data setcorrelating operation behaviors generated by a Control System.

In addition to the scheme outlined above, a custom design based onacquiring the user data sets in manufacturing process will furtherreduce design complexity and time to service and increase reliabilityand productivity as well. The user attribute data comprising thescenario-user-choice pair data set and the user profile data setcontaining data of one or more users whom a customer design targets areacquired and imported to the vehicle being manufactured, and areintegrated with the Control System and other parts of the vehicle,wherein simulations or road tests are run if needed, and the ControlSystem and other parts of the vehicle are tuned to an optimal conditionand settings of the vehicle are initialized according to specificationof the custom design before delivering the vehicle to a customer.

Based on the methods and procedures of customizing and legalizingself-driving motor vehicles described above, hereby is introduced asystem of customizing the operation of and legalizing self-driving motorvehicles, comprising:

Module 1, wherein a self-driving motor vehicle operating drivingaccording to a customized driving protocol; and

Module 2, wherein a manufacturer of a self-driving motor vehicle or anauthorized and/or designated organization and/or individual conductstests evaluating the performance of the self-driving motor vehicle inModule 1 and issues a performance certificate to the self-driving motorvehicle for applying for a license or a permit to provide a service tocarry a passenger, if the self-driving motor vehicle meets theperformance criterion by passing the tests comprised in Procedure Iand/or Procedure II described above.

In all, the methods and the system disclosed hereby should find themimplementable by ordinary skilled professionals in the field, and theapplicant would like to claim the rights and benefits to the scope ofthe disclosed invention as follows.

The invention claimed is:
 1. A system of a self-driving motor vehicleoperating by the method comprising: a self-driving motor vehicleobtaining a first data set of a rider of data of a plurality ofscenarios and data of an operation behavior preferred by the rider ineach of the plurality of scenarios, wherein the data in the first dataset are verifiable and lawful, finding a match between a currentscenario and a respective one of the scenarios of the plurality ofscenarios in the first data set and executing an operation in thecurrent scenario according to the operation behavior in the respectiveone of the scenarios in the first data set if the match is found, andthe first data set is used as a qualification for the self-driving motorvehicle to obtain a driving license or driving permit; a testingapparatus mounted on or being external to the self-driving motor vehicleconfigured to acquire the first data set.
 2. The system of claim 1,wherein the obtaining the data of the first data set comprises the stepsof: identifying the rider and then performing at least one of thefollowing steps: informing the rider before the self-driving motorvehicle conducts the operation according to the respective riderpreferred operation behavior in each of the plurality of scenarios, andthen obtaining a consent from the rider to responsibilities forconsequences of the operation according to the respective riderpreferred operation behaviors elected by the rider in each of theplurality of scenarios; or presenting to the rider a plurality ofscenarios in a training collection of data and one or more operationbehaviors of the self-driving vehicle in the plurality of scenarios inthe training collection of data, and then informing the rider ofresponsibilities for consequences of each of the operation behaviors inthe plurality of scenarios in the training collection of data, and thenobtaining a choice from the rider of one of the operation behaviors ineach of the plurality of scenarios of the training collection data toform a scenario-user-choice pair of the respective scenario and therespective rider preferred operation behavior, and then storing thescenario-user-choice pair in the first data set; or receiving previouslyacquired scenario-user-choice pairs of the rider into the first dataset.
 3. The system of claim 2, further comprising using the trainingcollection of data as a qualification for the self-driving motor vehicleto obtain a driving license or service permit to carry one or morepassengers.
 4. The system of claim 3, wherein the qualificationcomprises the plurality of scenarios in the training collection of databeing a sub-set of scenarios in a reference training collection, whereinthe scenarios in the reference training collection comprise availabledata of the plurality of scenarios wherein the rider electing arespective one of the rider preferred operation behaviors of theself-driving motor vehicle is required, and a ratio of a number of theplurality of scenarios in the training collection of data over a numberof the plurality of scenarios in the reference training collection beingbigger than a threshold value.
 5. The system of claim 1, wherein thequalification comprises the plurality of scenarios in the first data setbeing a sub-set of scenarios in a reference training collection, whereinthe scenarios in the reference training collection comprise availabledata of the plurality of scenarios wherein the rider electing one of therider preferred operation behaviors of the self-driving motor vehicle isrequired, and a ratio of a number of the plurality of scenarios in thefirst data set over a number of the plurality of scenarios in thereference training collection being bigger than a threshold value.
 6. Amethod of self-driving motor vehicle operation comprising the steps of:obtaining prior to a self-driving motor vehicle carrying one or morepassengers in practical services a first data set of a rider of data ofa plurality of scenarios and data of an operation behavior of theself-driving motor vehicle preferred by the rider in each of theplurality of scenarios, finding a match between a current scenario ofthe self-driving motor vehicle and a respective one of the plurality ofscenarios in the first data set and executing an operation of theself-driving motor vehicle in the current scenario according to theoperation behavior in the respective one of the plurality of scenariosin the first data set if the match is found, and the first data set isused as a qualification for the self-driving motor vehicle to obtain adriving license or driving permit.
 7. The method of claim 6, wherein theobtaining the data of the first data set comprises the steps of:identifying the rider and then performing at least one of the followingsteps: informing the rider before the self-driving motor vehicleconducts the operation according to the respective rider preferredoperation behavior in each of the plurality of scenarios, and thenobtaining a consent from the rider to responsibilities for consequencesof the operation according to the respective rider preferred operationbehaviors elected by the rider in each of the plurality of scenarios; orpresenting to the rider a plurality of scenarios in a trainingcollection of data and one or more operation behaviors of theself-driving vehicle in the plurality of scenarios in the trainingcollection of data, and then informing the rider of responsibilities forconsequences of each of the operation behaviors in the plurality ofscenarios in the training collection of data, and then obtaining achoice from the rider of one of the operation behaviors in each of theplurality of scenarios of the training collection data to form ascenario-user-choice pair of the respective scenario and the respectiverider preferred operation behavior, and then storing thescenario-user-choice pair in the first data set; or receiving previouslyacquired scenario-user-choice pairs of the rider into the first dataset.
 8. The method of claim 6, wherein the self-driving motor vehicleacquires a user profile data set, generates one or morenon-rider-specific operation behaviors of the self-driving motor vehicleif the match is not found, estimates a probability value for the riderto choose each of the non-rider-specific operation behaviors referencingdata of the rider in the user profile data set, and executes theoperation according to the non-rider-specific operation behavior havingthe largest probability value.
 9. The method of claim 7, furthercomprising using the training collection of data as a qualification forthe self-driving motor vehicle to obtain a driving license or servicepermit to carry one or more passengers.
 10. The method of claim 9,wherein the qualification comprises the plurality of scenarios in thetraining collection of data being a sub-set of scenarios in a referencetraining collection, wherein the scenarios in the reference trainingcollection comprise available data of the plurality of scenarios whereinthe rider electing a respective one of the rider preferred operationbehaviors of the self-driving motor vehicle is required, and a ratio ofa number of the plurality of scenarios in the training collection ofdata over a number of the plurality of scenarios in the referencetraining collection being bigger than a threshold value.
 11. The methodof claim 6, wherein the qualification comprises the plurality ofscenarios in the first data set being a sub-set of scenarios in areference training collection, wherein the scenarios in the referencetraining collection comprise available data of the plurality ofscenarios wherein the rider electing one of the rider preferredoperation behaviors of the self-driving motor vehicle is required, and aratio of a number of the plurality of scenarios in the first data setover a number of the plurality of scenarios in the reference trainingcollection being bigger than a threshold value.
 12. A method ofself-driving motor vehicle operation comprising the steps of: obtaininga first data set of a rider of data of a plurality of scenarios and dataof an operation behavior of a self-driving motor vehicle preferred bythe rider in each of the plurality of scenarios, finding a match betweena current scenario of the self-driving motor vehicle and a respectiveone of the plurality of scenarios in the first data set and executing anoperation of the self-driving motor vehicle in the current scenarioaccording to the operation behavior in the respective one of theplurality of scenarios in the first data set if the match is found, andthe first data set is used as a qualification for the self-driving motorvehicle to obtain a driving license or driving permit.
 13. The method ofclaim 12, wherein the obtaining the data of the first data set comprisesthe steps of: identifying the rider and then performing at least one ofthe following steps: informing the rider before the self-driving motorvehicle conducts the operation according to the respective riderpreferred operation behavior in each of the plurality of scenarios, andthen obtaining a consent from the rider to responsibilities forconsequences of the operation according to the respective riderpreferred operation behaviors elected by the rider in each of theplurality of scenarios; or presenting to the rider a plurality ofscenarios in a training collection of data and one or more operationbehaviors of the self-driving vehicle in the plurality of scenarios inthe training collection of data, and then informing the rider ofresponsibilities for consequences of each of the operation behaviors inthe plurality of scenarios in the training collection of data, and thenobtaining a choice from the rider of one of the operation behaviors ineach of the plurality of scenarios of the training collection data toform a scenario-user-choice pair of the respective scenario and therespective rider preferred operation behavior, and then storing thescenario-user-choice pair in the first data set; or receiving previouslyacquired scenario-user-choice pairs of the rider into the first dataset.
 14. The method of claim 13, further comprising using the trainingcollection of data as a qualification for the self-driving motor vehicleto obtain a driving license or service permit to carry one or morepassengers.
 15. The method of claim 14, wherein the qualificationcomprises the plurality of scenarios in the training collection of databeing a sub-set of scenarios in a reference training collection, whereinthe scenarios in the reference training collection comprise availabledata of the plurality of scenarios wherein the rider electing arespective one of the rider preferred operation behaviors of theself-driving motor vehicle is required, and a ratio of a number of theplurality of scenarios in the training collection of data over a numberof the plurality of scenarios in the reference training collection beingbigger than a threshold value.
 16. The method of claim 12, wherein thequalification comprises the plurality of scenarios in the first data setbeing a sub-set of scenarios in a reference training collection, whereinthe scenarios in the reference training collection comprise availabledata of the plurality of scenarios wherein the rider electing one of therider preferred operation behaviors of the self-driving motor vehicle isrequired, and a ratio of a number of the plurality of scenarios in thefirst data set over a number of the plurality of scenarios in thereference training collection being bigger than a threshold value. 17.The method of claim 12, wherein the executing the operation of theself-driving motor vehicle according to one of the rider preferredoperation behaviors in the plurality of scenarios in the first data setcomprises the rider assuming partial or full responsibilities forconsequences of the operation if the rider is a passenger on theself-driving motor vehicle.